Overview

Dataset statistics

Number of variables27
Number of observations9994
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory10.5 MiB
Average record size in memory1.1 KiB

Variable types

Categorical15
DateTime2
Numeric10

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
order_id has a high cardinality: 5009 distinct valuesHigh cardinality
customer_id has a high cardinality: 793 distinct valuesHigh cardinality
customer_name has a high cardinality: 793 distinct valuesHigh cardinality
city has a high cardinality: 531 distinct valuesHigh cardinality
product_id has a high cardinality: 1862 distinct valuesHigh cardinality
product_name has a high cardinality: 1850 distinct valuesHigh cardinality
sales is highly overall correlated with profit and 3 other fieldsHigh correlation
discount is highly overall correlated with profit and 1 other fieldsHigh correlation
profit is highly overall correlated with sales and 2 other fieldsHigh correlation
no_of_days_for_shipping is highly overall correlated with ship_modeHigh correlation
sale_price_per_unit is highly overall correlated with sales and 3 other fieldsHigh correlation
profit_per_unit is highly overall correlated with discount and 2 other fieldsHigh correlation
purchase_price_per_unit is highly overall correlated with sales and 2 other fieldsHigh correlation
purchase_price is highly overall correlated with sales and 2 other fieldsHigh correlation
month is highly overall correlated with month_nameHigh correlation
ship_mode is highly overall correlated with no_of_days_for_shippingHigh correlation
state is highly overall correlated with regionHigh correlation
region is highly overall correlated with stateHigh correlation
category is highly overall correlated with sub-categoryHigh correlation
sub-category is highly overall correlated with categoryHigh correlation
month_name is highly overall correlated with monthHigh correlation
discount has 4798 (48.0%) zerosZeros
no_of_days_for_shipping has 519 (5.2%) zerosZeros

Reproduction

Analysis started2023-06-13 02:03:50.441492
Analysis finished2023-06-13 02:04:17.928480
Duration27.49 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

order_id
Categorical

Distinct5009
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
CA-2017-100111
 
14
CA-2017-157987
 
12
CA-2016-165330
 
11
US-2016-108504
 
11
CA-2015-131338
 
10
Other values (5004)
9936 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters139916
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2538 ?
Unique (%)25.4%

Sample

1st rowCA-2016-152156
2nd rowCA-2016-152156
3rd rowCA-2016-138688
4th rowUS-2015-108966
5th rowUS-2015-108966

Common Values

ValueCountFrequency (%)
CA-2017-100111 14
 
0.1%
CA-2017-157987 12
 
0.1%
CA-2016-165330 11
 
0.1%
US-2016-108504 11
 
0.1%
CA-2015-131338 10
 
0.1%
CA-2016-105732 10
 
0.1%
US-2015-126977 10
 
0.1%
US-2016-114013 9
 
0.1%
CA-2014-106439 9
 
0.1%
CA-2016-145177 9
 
0.1%
Other values (4999) 9889
98.9%

Length

2023-06-13T02:04:18.070106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca-2017-100111 14
 
0.1%
ca-2017-157987 12
 
0.1%
ca-2016-165330 11
 
0.1%
us-2016-108504 11
 
0.1%
ca-2015-131338 10
 
0.1%
ca-2016-105732 10
 
0.1%
us-2015-126977 10
 
0.1%
us-2015-163433 9
 
0.1%
ca-2017-140949 9
 
0.1%
ca-2015-158421 9
 
0.1%
Other values (4999) 9889
98.9%

Most occurring characters

ValueCountFrequency (%)
1 25510
18.2%
- 19988
14.3%
0 15492
11.1%
2 15381
11.0%
C 8308
 
5.9%
A 8308
 
5.9%
6 7904
 
5.6%
7 7438
 
5.3%
4 7400
 
5.3%
5 7338
 
5.2%
Other values (5) 16849
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99940
71.4%
Dash Punctuation 19988
 
14.3%
Uppercase Letter 19988
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25510
25.5%
0 15492
15.5%
2 15381
15.4%
6 7904
 
7.9%
7 7438
 
7.4%
4 7400
 
7.4%
5 7338
 
7.3%
3 5449
 
5.5%
8 4042
 
4.0%
9 3986
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 8308
41.6%
A 8308
41.6%
U 1686
 
8.4%
S 1686
 
8.4%
Dash Punctuation
ValueCountFrequency (%)
- 19988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119928
85.7%
Latin 19988
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25510
21.3%
- 19988
16.7%
0 15492
12.9%
2 15381
12.8%
6 7904
 
6.6%
7 7438
 
6.2%
4 7400
 
6.2%
5 7338
 
6.1%
3 5449
 
4.5%
8 4042
 
3.4%
Latin
ValueCountFrequency (%)
C 8308
41.6%
A 8308
41.6%
U 1686
 
8.4%
S 1686
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25510
18.2%
- 19988
14.3%
0 15492
11.1%
2 15381
11.0%
C 8308
 
5.9%
A 8308
 
5.9%
6 7904
 
5.6%
7 7438
 
5.3%
4 7400
 
5.3%
5 7338
 
5.2%
Other values (5) 16849
12.0%
Distinct1237
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size414.2 KiB
Minimum2014-01-03 00:00:00
Maximum2017-12-30 00:00:00
2023-06-13T02:04:18.323790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:18.726779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1329
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size414.2 KiB
Minimum2014-01-07 00:00:00
Maximum2017-12-31 00:00:00
2023-06-13T02:04:19.089668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:19.483591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ship_mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1017.6 KiB
Standard Class
5968 
Second Class
1945 
First Class
1538 
Same Day
 
543

Length

Max length14
Median length14
Mean length12.823094
Min length8

Characters and Unicode

Total characters128154
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond Class
2nd rowSecond Class
3rd rowSecond Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 5968
59.7%
Second Class 1945
 
19.5%
First Class 1538
 
15.4%
Same Day 543
 
5.4%

Length

2023-06-13T02:04:19.885553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T02:04:20.240011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
class 9451
47.3%
standard 5968
29.9%
second 1945
 
9.7%
first 1538
 
7.7%
same 543
 
2.7%
day 543
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
l 9451
7.4%
C 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
r 7506
 
5.9%
t 7506
 
5.9%
Other values (8) 11083
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98172
76.6%
Uppercase Letter 19988
 
15.6%
Space Separator 9994
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22473
22.9%
s 20440
20.8%
d 13881
14.1%
l 9451
9.6%
n 7913
 
8.1%
r 7506
 
7.6%
t 7506
 
7.6%
e 2488
 
2.5%
c 1945
 
2.0%
o 1945
 
2.0%
Other values (3) 2624
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 9451
47.3%
S 8456
42.3%
F 1538
 
7.7%
D 543
 
2.7%
Space Separator
ValueCountFrequency (%)
9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118160
92.2%
Common 9994
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22473
19.0%
s 20440
17.3%
d 13881
11.7%
l 9451
8.0%
C 9451
8.0%
S 8456
 
7.2%
n 7913
 
6.7%
r 7506
 
6.4%
t 7506
 
6.4%
e 2488
 
2.1%
Other values (7) 8595
 
7.3%
Common
ValueCountFrequency (%)
9994
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
l 9451
7.4%
C 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
r 7506
 
5.9%
t 7506
 
5.9%
Other values (8) 11083
8.6%

customer_id
Categorical

Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size970.5 KiB
WB-21850
 
37
JL-15835
 
34
MA-17560
 
34
PP-18955
 
34
CK-12205
 
32
Other values (788)
9823 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters79952
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowCG-12520
2nd rowCG-12520
3rd rowDV-13045
4th rowSO-20335
5th rowSO-20335

Common Values

ValueCountFrequency (%)
WB-21850 37
 
0.4%
JL-15835 34
 
0.3%
MA-17560 34
 
0.3%
PP-18955 34
 
0.3%
CK-12205 32
 
0.3%
SV-20365 32
 
0.3%
JD-15895 32
 
0.3%
EH-13765 32
 
0.3%
ZC-21910 31
 
0.3%
EP-13915 31
 
0.3%
Other values (783) 9665
96.7%

Length

2023-06-13T02:04:20.579273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wb-21850 37
 
0.4%
ma-17560 34
 
0.3%
pp-18955 34
 
0.3%
jl-15835 34
 
0.3%
ck-12205 32
 
0.3%
sv-20365 32
 
0.3%
jd-15895 32
 
0.3%
eh-13765 32
 
0.3%
zc-21910 31
 
0.3%
ep-13915 31
 
0.3%
Other values (783) 9665
96.7%

Most occurring characters

ValueCountFrequency (%)
1 11915
14.9%
- 9994
12.5%
0 8532
 
10.7%
5 7865
 
9.8%
2 4682
 
5.9%
7 2931
 
3.7%
6 2909
 
3.6%
9 2904
 
3.6%
8 2818
 
3.5%
3 2779
 
3.5%
Other values (30) 22623
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49970
62.5%
Uppercase Letter 19945
 
24.9%
Dash Punctuation 9994
 
12.5%
Lowercase Letter 43
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1798
 
9.0%
C 1725
 
8.6%
M 1712
 
8.6%
B 1642
 
8.2%
D 1296
 
6.5%
A 1227
 
6.2%
J 1134
 
5.7%
P 1105
 
5.5%
H 968
 
4.9%
K 932
 
4.7%
Other values (16) 6406
32.1%
Decimal Number
ValueCountFrequency (%)
1 11915
23.8%
0 8532
17.1%
5 7865
15.7%
2 4682
 
9.4%
7 2931
 
5.9%
6 2909
 
5.8%
9 2904
 
5.8%
8 2818
 
5.6%
3 2779
 
5.6%
4 2635
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
p 29
67.4%
o 8
 
18.6%
l 6
 
14.0%
Dash Punctuation
ValueCountFrequency (%)
- 9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59964
75.0%
Latin 19988
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1798
 
9.0%
C 1725
 
8.6%
M 1712
 
8.6%
B 1642
 
8.2%
D 1296
 
6.5%
A 1227
 
6.1%
J 1134
 
5.7%
P 1105
 
5.5%
H 968
 
4.8%
K 932
 
4.7%
Other values (19) 6449
32.3%
Common
ValueCountFrequency (%)
1 11915
19.9%
- 9994
16.7%
0 8532
14.2%
5 7865
13.1%
2 4682
 
7.8%
7 2931
 
4.9%
6 2909
 
4.9%
9 2904
 
4.8%
8 2818
 
4.7%
3 2779
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11915
14.9%
- 9994
12.5%
0 8532
 
10.7%
5 7865
 
9.8%
2 4682
 
5.9%
7 2931
 
3.7%
6 2909
 
3.6%
9 2904
 
3.6%
8 2818
 
3.5%
3 2779
 
3.5%
Other values (30) 22623
28.3%

customer_name
Categorical

Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size1022.4 KiB
William Brown
 
37
John Lee
 
34
Matt Abelman
 
34
Paul Prost
 
34
Chloris Kastensmidt
 
32
Other values (788)
9823 

Length

Max length22
Median length18
Mean length12.960676
Min length7

Characters and Unicode

Total characters129529
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowClaire Gute
2nd rowClaire Gute
3rd rowDarrin Van Huff
4th rowSean O'Donnell
5th rowSean O'Donnell

Common Values

ValueCountFrequency (%)
William Brown 37
 
0.4%
John Lee 34
 
0.3%
Matt Abelman 34
 
0.3%
Paul Prost 34
 
0.3%
Chloris Kastensmidt 32
 
0.3%
Seth Vernon 32
 
0.3%
Jonathan Doherty 32
 
0.3%
Edward Hooks 32
 
0.3%
Zuschuss Carroll 31
 
0.3%
Emily Phan 31
 
0.3%
Other values (783) 9665
96.7%

Length

2023-06-13T02:04:21.017806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael 120
 
0.6%
frank 112
 
0.6%
john 107
 
0.5%
patrick 96
 
0.5%
brian 93
 
0.5%
stewart 93
 
0.5%
paul 92
 
0.5%
ken 91
 
0.5%
rick 91
 
0.5%
matt 86
 
0.4%
Other values (901) 19072
95.1%

Most occurring characters

ValueCountFrequency (%)
a 12011
 
9.3%
e 11836
 
9.1%
n 10241
 
7.9%
10059
 
7.8%
r 9530
 
7.4%
i 7919
 
6.1%
l 6494
 
5.0%
o 5850
 
4.5%
t 5435
 
4.2%
s 4546
 
3.5%
Other values (47) 45608
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98856
76.3%
Uppercase Letter 20461
 
15.8%
Space Separator 10059
 
7.8%
Other Punctuation 124
 
0.1%
Dash Punctuation 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12011
12.1%
e 11836
12.0%
n 10241
10.4%
r 9530
9.6%
i 7919
 
8.0%
l 6494
 
6.6%
o 5850
 
5.9%
t 5435
 
5.5%
s 4546
 
4.6%
h 3857
 
3.9%
Other values (18) 21137
21.4%
Uppercase Letter
ValueCountFrequency (%)
C 1830
 
8.9%
S 1798
 
8.8%
M 1749
 
8.5%
B 1696
 
8.3%
D 1325
 
6.5%
A 1282
 
6.3%
J 1134
 
5.5%
P 1105
 
5.4%
H 1005
 
4.9%
K 964
 
4.7%
Other values (16) 6573
32.1%
Space Separator
ValueCountFrequency (%)
10059
100.0%
Other Punctuation
ValueCountFrequency (%)
' 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119317
92.1%
Common 10212
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12011
 
10.1%
e 11836
 
9.9%
n 10241
 
8.6%
r 9530
 
8.0%
i 7919
 
6.6%
l 6494
 
5.4%
o 5850
 
4.9%
t 5435
 
4.6%
s 4546
 
3.8%
h 3857
 
3.2%
Other values (44) 41598
34.9%
Common
ValueCountFrequency (%)
10059
98.5%
' 124
 
1.2%
- 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129440
99.9%
None 89
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12011
 
9.3%
e 11836
 
9.1%
n 10241
 
7.9%
10059
 
7.8%
r 9530
 
7.4%
i 7919
 
6.1%
l 6494
 
5.0%
o 5850
 
4.5%
t 5435
 
4.2%
s 4546
 
3.5%
Other values (44) 45519
35.2%
None
ValueCountFrequency (%)
ö 61
68.5%
ä 23
 
25.8%
ü 5
 
5.6%

segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size978.7 KiB
Consumer
5191 
Corporate
3020 
Home Office
1783 

Length

Max length11
Median length8
Mean length8.8374024
Min length8

Characters and Unicode

Total characters88321
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowCorporate
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 5191
51.9%
Corporate 3020
30.2%
Home Office 1783
 
17.8%

Length

2023-06-13T02:04:21.469171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T02:04:21.823152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
consumer 5191
44.1%
corporate 3020
25.6%
home 1783
 
15.1%
office 1783
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
n 5191
 
5.9%
s 5191
 
5.9%
u 5191
 
5.9%
f 3566
 
4.0%
t 3020
 
3.4%
Other values (7) 14955
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74761
84.6%
Uppercase Letter 11777
 
13.3%
Space Separator 1783
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 13014
17.4%
e 11777
15.8%
r 11231
15.0%
m 6974
9.3%
n 5191
 
6.9%
s 5191
 
6.9%
u 5191
 
6.9%
f 3566
 
4.8%
t 3020
 
4.0%
p 3020
 
4.0%
Other values (3) 6586
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 8211
69.7%
H 1783
 
15.1%
O 1783
 
15.1%
Space Separator
ValueCountFrequency (%)
1783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86538
98.0%
Common 1783
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 13014
15.0%
e 11777
13.6%
r 11231
13.0%
C 8211
9.5%
m 6974
8.1%
n 5191
 
6.0%
s 5191
 
6.0%
u 5191
 
6.0%
f 3566
 
4.1%
t 3020
 
3.5%
Other values (6) 13172
15.2%
Common
ValueCountFrequency (%)
1783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
n 5191
 
5.9%
s 5191
 
5.9%
u 5191
 
5.9%
f 3566
 
4.0%
t 3020
 
3.4%
Other values (7) 14955
16.9%

city
Categorical

Distinct531
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size983.5 KiB
New York City
915 
Los Angeles
747 
Philadelphia
 
537
San Francisco
 
510
Seattle
 
428
Other values (526)
6857 

Length

Max length17
Median length14
Mean length9.3306984
Min length4

Characters and Unicode

Total characters93251
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.7%

Sample

1st rowHenderson
2nd rowHenderson
3rd rowLos Angeles
4th rowFort Lauderdale
5th rowFort Lauderdale

Common Values

ValueCountFrequency (%)
New York City 915
 
9.2%
Los Angeles 747
 
7.5%
Philadelphia 537
 
5.4%
San Francisco 510
 
5.1%
Seattle 428
 
4.3%
Houston 377
 
3.8%
Chicago 314
 
3.1%
Columbus 222
 
2.2%
San Diego 170
 
1.7%
Springfield 163
 
1.6%
Other values (521) 5611
56.1%

Length

2023-06-13T02:04:22.062428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 994
 
7.0%
new 937
 
6.6%
york 920
 
6.5%
san 805
 
5.7%
los 747
 
5.2%
angeles 747
 
5.2%
philadelphia 537
 
3.8%
francisco 510
 
3.6%
seattle 428
 
3.0%
houston 377
 
2.6%
Other values (555) 7234
50.8%

Most occurring characters

ValueCountFrequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74773
80.2%
Uppercase Letter 14236
 
15.3%
Space Separator 4242
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8719
11.7%
a 7591
10.2%
o 7499
10.0%
i 6229
 
8.3%
n 6199
 
8.3%
l 5986
 
8.0%
s 4699
 
6.3%
r 4468
 
6.0%
t 4438
 
5.9%
c 2393
 
3.2%
Other values (16) 16552
22.1%
Uppercase Letter
ValueCountFrequency (%)
C 2085
14.6%
S 1740
12.2%
L 1295
9.1%
A 1242
8.7%
N 1134
8.0%
P 1013
 
7.1%
Y 940
 
6.6%
F 794
 
5.6%
D 627
 
4.4%
H 617
 
4.3%
Other values (14) 2749
19.3%
Space Separator
ValueCountFrequency (%)
4242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89009
95.5%
Common 4242
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8719
 
9.8%
a 7591
 
8.5%
o 7499
 
8.4%
i 6229
 
7.0%
n 6199
 
7.0%
l 5986
 
6.7%
s 4699
 
5.3%
r 4468
 
5.0%
t 4438
 
5.0%
c 2393
 
2.7%
Other values (40) 30788
34.6%
Common
ValueCountFrequency (%)
4242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

state
Categorical

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size975.3 KiB
California
2001 
New York
1128 
Texas
985 
Pennsylvania
587 
Washington
506 
Other values (44)
4787 

Length

Max length20
Median length14
Mean length8.4871923
Min length4

Characters and Unicode

Total characters84821
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKentucky
2nd rowKentucky
3rd rowCalifornia
4th rowFlorida
5th rowFlorida

Common Values

ValueCountFrequency (%)
California 2001
20.0%
New York 1128
 
11.3%
Texas 985
 
9.9%
Pennsylvania 587
 
5.9%
Washington 506
 
5.1%
Illinois 492
 
4.9%
Ohio 469
 
4.7%
Florida 383
 
3.8%
Michigan 255
 
2.6%
North Carolina 249
 
2.5%
Other values (39) 2939
29.4%

Length

2023-06-13T02:04:22.495695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 2001
17.1%
new 1322
 
11.3%
york 1128
 
9.6%
texas 985
 
8.4%
pennsylvania 587
 
5.0%
washington 506
 
4.3%
illinois 492
 
4.2%
ohio 469
 
4.0%
florida 383
 
3.3%
carolina 291
 
2.5%
Other values (43) 3542
30.3%

Most occurring characters

ValueCountFrequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71413
84.2%
Uppercase Letter 11696
 
13.8%
Space Separator 1712
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10758
15.1%
i 9895
13.9%
n 8090
11.3%
o 7323
10.3%
r 5544
7.8%
e 5051
7.1%
l 4822
6.8%
s 4604
6.4%
f 2011
 
2.8%
h 1898
 
2.7%
Other values (14) 11417
16.0%
Uppercase Letter
ValueCountFrequency (%)
C 2566
21.9%
N 1655
14.2%
T 1168
10.0%
Y 1128
9.6%
M 763
 
6.5%
I 748
 
6.4%
O 659
 
5.6%
W 621
 
5.3%
P 587
 
5.0%
F 383
 
3.3%
Other values (11) 1418
12.1%
Space Separator
ValueCountFrequency (%)
1712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83109
98.0%
Common 1712
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10758
12.9%
i 9895
11.9%
n 8090
 
9.7%
o 7323
 
8.8%
r 5544
 
6.7%
e 5051
 
6.1%
l 4822
 
5.8%
s 4604
 
5.5%
C 2566
 
3.1%
f 2011
 
2.4%
Other values (35) 22445
27.0%
Common
ValueCountFrequency (%)
1712
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size939.9 KiB
West
3203 
East
2848 
Central
2323 
South
1620 

Length

Max length7
Median length4
Mean length4.8594156
Min length4

Characters and Unicode

Total characters48565
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowWest
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
West 3203
32.0%
East 2848
28.5%
Central 2323
23.2%
South 1620
16.2%

Length

2023-06-13T02:04:22.855737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T02:04:23.038127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
west 3203
32.0%
east 2848
28.5%
central 2323
23.2%
south 1620
16.2%

Most occurring characters

ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38571
79.4%
Uppercase Letter 9994
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9994
25.9%
s 6051
15.7%
e 5526
14.3%
a 5171
13.4%
n 2323
 
6.0%
r 2323
 
6.0%
l 2323
 
6.0%
o 1620
 
4.2%
u 1620
 
4.2%
h 1620
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
W 3203
32.0%
E 2848
28.5%
C 2323
23.2%
S 1620
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 48565
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

product_id
Categorical

Distinct1862
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
OFF-PA-10001970
 
19
TEC-AC-10003832
 
18
FUR-FU-10004270
 
16
FUR-CH-10001146
 
15
TEC-AC-10003628
 
15
Other values (1857)
9911 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters149910
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowFUR-BO-10001798
2nd rowFUR-CH-10000454
3rd rowOFF-LA-10000240
4th rowFUR-TA-10000577
5th rowOFF-ST-10000760

Common Values

ValueCountFrequency (%)
OFF-PA-10001970 19
 
0.2%
TEC-AC-10003832 18
 
0.2%
FUR-FU-10004270 16
 
0.2%
FUR-CH-10001146 15
 
0.2%
TEC-AC-10003628 15
 
0.2%
FUR-CH-10002647 15
 
0.2%
TEC-AC-10002049 15
 
0.2%
OFF-BI-10004632 14
 
0.1%
OFF-BI-10001524 14
 
0.1%
FUR-CH-10002880 14
 
0.1%
Other values (1852) 9839
98.4%

Length

2023-06-13T02:04:23.189932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
off-pa-10001970 19
 
0.2%
tec-ac-10003832 18
 
0.2%
fur-fu-10004270 16
 
0.2%
fur-ch-10001146 15
 
0.2%
tec-ac-10003628 15
 
0.2%
fur-ch-10002647 15
 
0.2%
tec-ac-10002049 15
 
0.2%
off-pa-10002377 14
 
0.1%
fur-ch-10003774 14
 
0.1%
off-bi-10002026 14
 
0.1%
Other values (1852) 9839
98.4%

Most occurring characters

ValueCountFrequency (%)
0 35052
23.4%
- 19988
13.3%
F 15347
10.2%
1 14995
10.0%
O 6322
 
4.2%
2 4862
 
3.2%
4 4831
 
3.2%
3 4805
 
3.2%
A 4422
 
2.9%
5 3401
 
2.3%
Other values (17) 35885
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79952
53.3%
Uppercase Letter 49970
33.3%
Dash Punctuation 19988
 
13.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 15347
30.7%
O 6322
12.7%
A 4422
 
8.8%
C 3307
 
6.6%
U 3268
 
6.5%
T 3012
 
6.0%
R 2917
 
5.8%
P 2725
 
5.5%
E 2101
 
4.2%
B 1751
 
3.5%
Other values (6) 4798
 
9.6%
Decimal Number
ValueCountFrequency (%)
0 35052
43.8%
1 14995
18.8%
2 4862
 
6.1%
4 4831
 
6.0%
3 4805
 
6.0%
5 3401
 
4.3%
7 3103
 
3.9%
9 3051
 
3.8%
6 2999
 
3.8%
8 2853
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 19988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99940
66.7%
Latin 49970
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 15347
30.7%
O 6322
12.7%
A 4422
 
8.8%
C 3307
 
6.6%
U 3268
 
6.5%
T 3012
 
6.0%
R 2917
 
5.8%
P 2725
 
5.5%
E 2101
 
4.2%
B 1751
 
3.5%
Other values (6) 4798
 
9.6%
Common
ValueCountFrequency (%)
0 35052
35.1%
- 19988
20.0%
1 14995
15.0%
2 4862
 
4.9%
4 4831
 
4.8%
3 4805
 
4.8%
5 3401
 
3.4%
7 3103
 
3.1%
9 3051
 
3.1%
6 2999
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35052
23.4%
- 19988
13.3%
F 15347
10.2%
1 14995
10.0%
O 6322
 
4.2%
2 4862
 
3.2%
4 4831
 
3.2%
3 4805
 
3.2%
A 4422
 
2.9%
5 3401
 
2.3%
Other values (17) 35885
23.9%

category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1017.4 KiB
Office Supplies
6026 
Furniture
2121 
Technology
1847 

Length

Max length15
Median length15
Mean length12.802582
Min length9

Characters and Unicode

Total characters127949
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture
2nd rowFurniture
3rd rowOffice Supplies
4th rowFurniture
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 6026
60.3%
Furniture 2121
 
21.2%
Technology 1847
 
18.5%

Length

2023-06-13T02:04:23.368878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T02:04:23.526932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
office 6026
37.6%
supplies 6026
37.6%
furniture 2121
 
13.2%
technology 1847
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
s 6026
 
4.7%
S 6026
 
4.7%
Other values (10) 29560
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 105903
82.8%
Uppercase Letter 16020
 
12.5%
Space Separator 6026
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16020
15.1%
i 14173
13.4%
p 12052
11.4%
f 12052
11.4%
u 10268
9.7%
c 7873
7.4%
l 7873
7.4%
s 6026
 
5.7%
r 4242
 
4.0%
n 3968
 
3.7%
Other values (5) 11356
10.7%
Uppercase Letter
ValueCountFrequency (%)
O 6026
37.6%
S 6026
37.6%
F 2121
 
13.2%
T 1847
 
11.5%
Space Separator
ValueCountFrequency (%)
6026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121923
95.3%
Common 6026
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16020
13.1%
i 14173
11.6%
p 12052
9.9%
f 12052
9.9%
u 10268
8.4%
c 7873
 
6.5%
l 7873
 
6.5%
O 6026
 
4.9%
s 6026
 
4.9%
S 6026
 
4.9%
Other values (9) 23534
19.3%
Common
ValueCountFrequency (%)
6026
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
s 6026
 
4.7%
S 6026
 
4.7%
Other values (10) 29560
23.1%

sub-category
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size962.6 KiB
Binders
1523 
Paper
1370 
Furnishings
957 
Phones
889 
Storage
846 
Other values (12)
4409 

Length

Max length11
Median length9
Mean length7.191715
Min length3

Characters and Unicode

Total characters71874
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBookcases
2nd rowChairs
3rd rowLabels
4th rowTables
5th rowStorage

Common Values

ValueCountFrequency (%)
Binders 1523
15.2%
Paper 1370
13.7%
Furnishings 957
9.6%
Phones 889
8.9%
Storage 846
8.5%
Art 796
8.0%
Accessories 775
7.8%
Chairs 617
6.2%
Appliances 466
 
4.7%
Labels 364
 
3.6%
Other values (7) 1391
13.9%

Length

2023-06-13T02:04:23.690575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 1523
15.2%
paper 1370
13.7%
furnishings 957
9.6%
phones 889
8.9%
storage 846
8.5%
art 796
8.0%
accessories 775
7.8%
chairs 617
6.2%
appliances 466
 
4.7%
labels 364
 
3.6%
Other values (7) 1391
13.9%

Most occurring characters

ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61880
86.1%
Uppercase Letter 9994
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9934
16.1%
e 8870
14.3%
r 7169
11.6%
i 5668
9.2%
n 5378
8.7%
a 4542
7.3%
o 3288
 
5.3%
p 3004
 
4.9%
h 2578
 
4.2%
c 2359
 
3.8%
Other values (8) 9090
14.7%
Uppercase Letter
ValueCountFrequency (%)
P 2259
22.6%
A 2037
20.4%
B 1751
17.5%
F 1174
11.7%
S 1036
10.4%
C 685
 
6.9%
L 364
 
3.6%
T 319
 
3.2%
E 254
 
2.5%
M 115
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 71874
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

product_name
Categorical

Distinct1850
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Staple envelope
 
48
Staples
 
46
Easy-staple paper
 
46
Avery Non-Stick Binders
 
20
Staples in misc. colors
 
19
Other values (1845)
9815 

Length

Max length127
Median length78
Mean length36.91605
Min length5

Characters and Unicode

Total characters368939
Distinct characters85
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowBush Somerset Collection Bookcase
2nd rowHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back
3rd rowSelf-Adhesive Address Labels for Typewriters by Universal
4th rowBretford CR4500 Series Slim Rectangular Table
5th rowEldon Fold 'N Roll Cart System

Common Values

ValueCountFrequency (%)
Staple envelope 48
 
0.5%
Staples 46
 
0.5%
Easy-staple paper 46
 
0.5%
Avery Non-Stick Binders 20
 
0.2%
Staples in misc. colors 19
 
0.2%
Staple remover 18
 
0.2%
KI Adjustable-Height Table 18
 
0.2%
Storex Dura Pro Binders 17
 
0.2%
Staple-based wall hangings 16
 
0.2%
Logitech 910-002974 M325 Wireless Mouse for Web Scrolling 15
 
0.2%
Other values (1840) 9731
97.4%

Length

2023-06-13T02:04:23.913958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xerox 865
 
1.5%
x 701
 
1.3%
599
 
1.1%
with 599
 
1.1%
avery 557
 
1.0%
for 539
 
1.0%
binders 524
 
0.9%
chair 479
 
0.9%
black 426
 
0.8%
phone 374
 
0.7%
Other values (2798) 50371
89.9%

Most occurring characters

ValueCountFrequency (%)
45670
 
12.4%
e 33538
 
9.1%
r 20791
 
5.6%
o 19902
 
5.4%
a 19064
 
5.2%
i 18648
 
5.1%
l 16365
 
4.4%
n 15622
 
4.2%
s 14683
 
4.0%
t 14550
 
3.9%
Other values (75) 150106
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 238253
64.6%
Uppercase Letter 56270
 
15.3%
Space Separator 46097
 
12.5%
Decimal Number 17981
 
4.9%
Other Punctuation 7152
 
1.9%
Dash Punctuation 2940
 
0.8%
Control 86
 
< 0.1%
Close Punctuation 60
 
< 0.1%
Open Punctuation 60
 
< 0.1%
Math Symbol 35
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33538
14.1%
r 20791
 
8.7%
o 19902
 
8.4%
a 19064
 
8.0%
i 18648
 
7.8%
l 16365
 
6.9%
n 15622
 
6.6%
s 14683
 
6.2%
t 14550
 
6.1%
c 8924
 
3.7%
Other values (18) 56166
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 6281
 
11.2%
C 6007
 
10.7%
B 5530
 
9.8%
P 4918
 
8.7%
A 2948
 
5.2%
D 2941
 
5.2%
M 2870
 
5.1%
T 2616
 
4.6%
F 2510
 
4.5%
L 2284
 
4.1%
Other values (16) 17365
30.9%
Other Punctuation
ValueCountFrequency (%)
, 3120
43.6%
/ 1561
21.8%
" 1300
18.2%
. 463
 
6.5%
& 287
 
4.0%
' 257
 
3.6%
# 90
 
1.3%
% 45
 
0.6%
* 9
 
0.1%
! 9
 
0.1%
Other values (2) 11
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 3783
21.0%
0 2921
16.2%
2 2270
12.6%
4 1725
9.6%
3 1530
8.5%
5 1443
 
8.0%
8 1254
 
7.0%
9 1234
 
6.9%
6 941
 
5.2%
7 880
 
4.9%
Space Separator
ValueCountFrequency (%)
45670
99.1%
  427
 
0.9%
Control
ValueCountFrequency (%)
” 67
77.9%
“ 19
 
22.1%
Dash Punctuation
ValueCountFrequency (%)
- 2940
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Math Symbol
ValueCountFrequency (%)
+ 35
100.0%
Other Number
ValueCountFrequency (%)
¾ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 294523
79.8%
Common 74416
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33538
 
11.4%
r 20791
 
7.1%
o 19902
 
6.8%
a 19064
 
6.5%
i 18648
 
6.3%
l 16365
 
5.6%
n 15622
 
5.3%
s 14683
 
5.0%
t 14550
 
4.9%
c 8924
 
3.0%
Other values (44) 112436
38.2%
Common
ValueCountFrequency (%)
45670
61.4%
1 3783
 
5.1%
, 3120
 
4.2%
- 2940
 
4.0%
0 2921
 
3.9%
2 2270
 
3.1%
4 1725
 
2.3%
/ 1561
 
2.1%
3 1530
 
2.1%
5 1443
 
1.9%
Other values (21) 7453
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368404
99.9%
None 535
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45670
 
12.4%
e 33538
 
9.1%
r 20791
 
5.6%
o 19902
 
5.4%
a 19064
 
5.2%
i 18648
 
5.1%
l 16365
 
4.4%
n 15622
 
4.2%
s 14683
 
4.0%
t 14550
 
3.9%
Other values (69) 149571
40.6%
None
ValueCountFrequency (%)
  427
79.8%
” 67
 
12.5%
“ 19
 
3.6%
é 14
 
2.6%
¾ 5
 
0.9%
à 3
 
0.6%

sales
Real number (ℝ)

Distinct5825
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.858
Minimum0.444
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2023-06-13T02:04:24.126511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile4.98
Q117.28
median54.49
Q3209.94
95-th percentile956.98425
Maximum22638.48
Range22638.036
Interquartile range (IQR)192.66

Descriptive statistics

Standard deviation623.2451
Coefficient of variation (CV)2.7114353
Kurtosis305.31175
Mean229.858
Median Absolute Deviation (MAD)45.406
Skewness12.972752
Sum2297200.9
Variance388434.46
MonotonicityNot monotonic
2023-06-13T02:04:24.343238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 56
 
0.6%
19.44 39
 
0.4%
15.552 39
 
0.4%
25.92 36
 
0.4%
10.368 36
 
0.4%
32.4 28
 
0.3%
17.94 21
 
0.2%
6.48 21
 
0.2%
20.736 19
 
0.2%
14.94 17
 
0.2%
Other values (5815) 9682
96.9%
ValueCountFrequency (%)
0.444 1
 
< 0.1%
0.556 1
 
< 0.1%
0.836 1
 
< 0.1%
0.852 1
 
< 0.1%
0.876 1
 
< 0.1%
0.898 1
 
< 0.1%
0.984 1
 
< 0.1%
0.99 1
 
< 0.1%
1.044 1
 
< 0.1%
1.08 3
< 0.1%
ValueCountFrequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.976 1
< 0.1%

quantity
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7895737
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2023-06-13T02:04:24.498364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2251097
Coefficient of variation (CV)0.58716622
Kurtosis1.9918894
Mean3.7895737
Median Absolute Deviation (MAD)1
Skewness1.2785448
Sum37873
Variance4.9511131
MonotonicityNot monotonic
2023-06-13T02:04:24.650072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2409
24.1%
2 2402
24.0%
5 1230
12.3%
4 1191
11.9%
1 899
 
9.0%
7 606
 
6.1%
6 572
 
5.7%
9 258
 
2.6%
8 257
 
2.6%
10 57
 
0.6%
Other values (4) 113
 
1.1%
ValueCountFrequency (%)
1 899
 
9.0%
2 2402
24.0%
3 2409
24.1%
4 1191
11.9%
5 1230
12.3%
6 572
 
5.7%
7 606
 
6.1%
8 257
 
2.6%
9 258
 
2.6%
10 57
 
0.6%
ValueCountFrequency (%)
14 29
 
0.3%
13 27
 
0.3%
12 23
 
0.2%
11 34
 
0.3%
10 57
 
0.6%
9 258
 
2.6%
8 257
 
2.6%
7 606
6.1%
6 572
5.7%
5 1230
12.3%

discount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15620272
Minimum0
Maximum0.8
Zeros4798
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2023-06-13T02:04:24.807227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20645197
Coefficient of variation (CV)1.3216925
Kurtosis2.4095461
Mean0.15620272
Median Absolute Deviation (MAD)0.2
Skewness1.6842947
Sum1561.09
Variance0.042622415
MonotonicityNot monotonic
2023-06-13T02:04:24.969508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4798
48.0%
0.2 3657
36.6%
0.7 418
 
4.2%
0.8 300
 
3.0%
0.3 227
 
2.3%
0.4 206
 
2.1%
0.6 138
 
1.4%
0.1 94
 
0.9%
0.5 66
 
0.7%
0.15 52
 
0.5%
Other values (2) 38
 
0.4%
ValueCountFrequency (%)
0 4798
48.0%
0.1 94
 
0.9%
0.15 52
 
0.5%
0.2 3657
36.6%
0.3 227
 
2.3%
0.32 27
 
0.3%
0.4 206
 
2.1%
0.45 11
 
0.1%
0.5 66
 
0.7%
0.6 138
 
1.4%
ValueCountFrequency (%)
0.8 300
 
3.0%
0.7 418
 
4.2%
0.6 138
 
1.4%
0.5 66
 
0.7%
0.45 11
 
0.1%
0.4 206
 
2.1%
0.32 27
 
0.3%
0.3 227
 
2.3%
0.2 3657
36.6%
0.15 52
 
0.5%

profit
Real number (ℝ)

Distinct7287
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.656896
Minimum-6599.978
Maximum8399.976
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size414.2 KiB
2023-06-13T02:04:25.159223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-53.03092
Q11.72875
median8.6665
Q329.364
95-th percentile168.4704
Maximum8399.976
Range14999.954
Interquartile range (IQR)27.63525

Descriptive statistics

Standard deviation234.26011
Coefficient of variation (CV)8.1746504
Kurtosis397.18851
Mean28.656896
Median Absolute Deviation (MAD)10.77855
Skewness7.5614316
Sum286397.02
Variance54877.798
MonotonicityNot monotonic
2023-06-13T02:04:25.366910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.7%
6.2208 43
 
0.4%
9.3312 38
 
0.4%
5.4432 32
 
0.3%
3.6288 32
 
0.3%
15.552 26
 
0.3%
12.4416 21
 
0.2%
7.2576 19
 
0.2%
3.1104 18
 
0.2%
9.072 11
 
0.1%
Other values (7277) 9689
96.9%
ValueCountFrequency (%)
-6599.978 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2639.9912 1
< 0.1%
-2287.782 1
< 0.1%
-1862.3124 1
< 0.1%
-1850.9464 1
< 0.1%
-1811.0784 1
< 0.1%
ValueCountFrequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2799.984 1
< 0.1%
2591.9568 1
< 0.1%
2504.2216 1
< 0.1%

no_of_days_for_shipping
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9581749
Minimum0
Maximum7
Zeros519
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2023-06-13T02:04:25.535344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7475667
Coefficient of variation (CV)0.44150822
Kurtosis-0.28755198
Mean3.9581749
Median Absolute Deviation (MAD)1
Skewness-0.42132235
Sum39558
Variance3.0539895
MonotonicityNot monotonic
2023-06-13T02:04:25.671066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 2774
27.8%
5 2169
21.7%
2 1334
13.3%
6 1203
12.0%
3 1005
 
10.1%
7 621
 
6.2%
0 519
 
5.2%
1 369
 
3.7%
ValueCountFrequency (%)
0 519
 
5.2%
1 369
 
3.7%
2 1334
13.3%
3 1005
 
10.1%
4 2774
27.8%
5 2169
21.7%
6 1203
12.0%
7 621
 
6.2%
ValueCountFrequency (%)
7 621
 
6.2%
6 1203
12.0%
5 2169
21.7%
4 2774
27.8%
3 1005
 
10.1%
2 1334
13.3%
1 369
 
3.7%
0 519
 
5.2%

sale_price_per_unit
Real number (ℝ)

Distinct5006
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.862121
Minimum-0.356
Maximum3772.9967
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size414.2 KiB
2023-06-13T02:04:25.862856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.356
5-th percentile1.82
Q15.402
median16.236
Q363.8845
95-th percentile243.9618
Maximum3772.9967
Range3773.3527
Interquartile range (IQR)58.4825

Descriptive statistics

Standard deviation142.93014
Coefficient of variation (CV)2.3484253
Kurtosis197.79112
Mean60.862121
Median Absolute Deviation (MAD)13.333
Skewness10.78231
Sum608256.04
Variance20429.026
MonotonicityNot monotonic
2023-06-13T02:04:26.062316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.48 158
 
1.6%
5.98 49
 
0.5%
5.117333333 39
 
0.4%
6.48 38
 
0.4%
2.88 36
 
0.4%
5.084 36
 
0.4%
6.68 33
 
0.3%
4.13 32
 
0.3%
29.99 30
 
0.3%
4.28 30
 
0.3%
Other values (4996) 9513
95.2%
ValueCountFrequency (%)
-0.356 1
< 0.1%
-0.244 1
< 0.1%
0.036 1
< 0.1%
0.076 1
< 0.1%
0.092 1
< 0.1%
0.09333333333 1
< 0.1%
0.098 1
< 0.1%
0.136 1
< 0.1%
0.1493333333 1
< 0.1%
0.152 1
< 0.1%
ValueCountFrequency (%)
3772.996667 1
< 0.1%
3499.99 1
< 0.1%
3499.99 2
< 0.1%
2799.942 1
< 0.1%
2399.892 1
< 0.1%
2099.894 1
< 0.1%
1999.87 1
< 0.1%
1995.99 1
< 0.1%
1889.99 1
< 0.1%
1749.99 1
< 0.1%

profit_per_unit
Real number (ℝ)

Distinct4246
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7993719
Minimum-1319.9956
Maximum1679.9952
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size414.2 KiB
2023-06-13T02:04:26.271889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1319.9956
5-th percentile-15.7514
Q10.7228
median2.767
Q38.7032
95-th percentile44.9985
Maximum1679.9952
Range2999.9908
Interquartile range (IQR)7.9804

Descriptive statistics

Standard deviation56.074974
Coefficient of variation (CV)7.1896781
Kurtosis355.61647
Mean7.7993719
Median Absolute Deviation (MAD)3.1531
Skewness7.6224049
Sum77946.923
Variance3144.4027
MonotonicityNot monotonic
2023-06-13T02:04:26.473492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1104 125
 
1.3%
1.8144 104
 
1.0%
0 65
 
0.7%
3.1104 51
 
0.5%
3.2064 25
 
0.3%
7.745 23
 
0.2%
5.7716 22
 
0.2%
0.9512 22
 
0.2%
1.4112 22
 
0.2%
3.4357 21
 
0.2%
Other values (4236) 9514
95.2%
ValueCountFrequency (%)
-1319.9956 2
< 0.1%
-959.9976 1
< 0.1%
-679.996 1
< 0.1%
-585.8969 1
< 0.1%
-462.7366 2
< 0.1%
-381.297 2
< 0.1%
-326.6376 1
< 0.1%
-314.9982 1
< 0.1%
-301.8464 1
< 0.1%
-296.0067 1
< 0.1%
ValueCountFrequency (%)
1679.9952 3
< 0.1%
997.995 1
 
< 0.1%
979.9972 1
 
< 0.1%
926.0951 1
 
< 0.1%
635.495 4
< 0.1%
626.0554 1
 
< 0.1%
559.9968 2
 
< 0.1%
557.256 2
 
< 0.1%
548.0971 1
 
< 0.1%
421.5853 5
0.1%

purchase_price_per_unit
Real number (ℝ)

Distinct6078
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.062749
Minimum0.4544
Maximum4074.8431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2023-06-13T02:04:26.691417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.4544
5-th percentile1.60808
Q13.4324
median12.7859
Q354.522
95-th percentile217.4069
Maximum4074.8431
Range4074.3887
Interquartile range (IQR)51.0896

Descriptive statistics

Standard deviation122.24696
Coefficient of variation (CV)2.3038188
Kurtosis217.50886
Mean53.062749
Median Absolute Deviation (MAD)10.471833
Skewness10.707819
Sum530309.12
Variance14944.319
MonotonicityNot monotonic
2023-06-13T02:04:26.897683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3696 89
 
0.9%
3.3696 51
 
0.5%
3.3696 36
 
0.4%
3.2696 32
 
0.3%
3.302933333 32
 
0.3%
3.4736 25
 
0.3%
3.8743 21
 
0.2%
17.039 20
 
0.2%
3.3196 19
 
0.2%
6.5084 19
 
0.2%
Other values (6068) 9650
96.6%
ValueCountFrequency (%)
0.4544 1
< 0.1%
0.4778333333 2
< 0.1%
0.5042 1
< 0.1%
0.5258285714 1
< 0.1%
0.5445 2
< 0.1%
0.5445 1
< 0.1%
0.5544 2
< 0.1%
0.5544 1
< 0.1%
0.5572 1
< 0.1%
0.5708666667 1
< 0.1%
ValueCountFrequency (%)
4074.843067 1
< 0.1%
2959.8676 1
< 0.1%
2219.8926 1
< 0.1%
2219.8526 1
< 0.1%
2219.6426 1
< 0.1%
1819.9948 1
< 0.1%
1819.9948 2
< 0.1%
1819.9448 1
< 0.1%
1819.8948 1
< 0.1%
1572.0288 2
< 0.1%

purchase_price
Real number (ℝ)

Distinct7642
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.0449
Minimum0.5544
Maximum24449.058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2023-06-13T02:04:27.617746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.5544
5-th percentile3.8964
Q112.4967
median41.577
Q3181.9095
95-th percentile862.80072
Maximum24449.058
Range24448.504
Interquartile range (IQR)169.4128

Descriptive statistics

Standard deviation550.82677
Coefficient of variation (CV)2.7398196
Kurtosis454.60542
Mean201.0449
Median Absolute Deviation (MAD)35.1384
Skewness14.753314
Sum2009242.7
Variance303410.13
MonotonicityNot monotonic
2023-06-13T02:04:27.824373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7392 43
 
0.4%
10.1088 38
 
0.4%
9.9088 32
 
0.3%
6.5392 32
 
0.3%
16.848 26
 
0.3%
13.4784 21
 
0.2%
13.2784 19
 
0.2%
3.3696 18
 
0.2%
20.0176 10
 
0.1%
7.7486 10
 
0.1%
Other values (7632) 9745
97.5%
ValueCountFrequency (%)
0.5544 1
< 0.1%
0.64 2
< 0.1%
0.6572 1
< 0.1%
0.6736 1
< 0.1%
0.7012 1
< 0.1%
0.736 1
< 0.1%
0.7484 1
< 0.1%
0.754 1
< 0.1%
0.84 1
< 0.1%
0.8812 1
< 0.1%
ValueCountFrequency (%)
24449.0584 1
< 0.1%
11839.4704 1
< 0.1%
11099.263 1
< 0.1%
9519.544 1
< 0.1%
9099.974 1
< 0.1%
7860.144 1
< 0.1%
7279.9792 1
< 0.1%
7279.7792 1
< 0.1%
7279.5792 1
< 0.1%
6733.9482 1
< 0.1%

weekday_string
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size962.7 KiB
Wednesday
1593 
Tuesday
1566 
Friday
1562 
Saturday
1429 
Thursday
1415 
Other values (2)
2429 

Length

Max length9
Median length8
Mean length7.2040224
Min length6

Characters and Unicode

Total characters71997
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowFriday
3rd rowThursday
4th rowSunday
5th rowSunday

Common Values

ValueCountFrequency (%)
Wednesday 1593
15.9%
Tuesday 1566
15.7%
Friday 1562
15.6%
Saturday 1429
14.3%
Thursday 1415
14.2%
Monday 1241
12.4%
Sunday 1188
11.9%

Length

2023-06-13T02:04:28.025581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T02:04:28.218663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
wednesday 1593
15.9%
tuesday 1566
15.7%
friday 1562
15.6%
saturday 1429
14.3%
thursday 1415
14.2%
monday 1241
12.4%
sunday 1188
11.9%

Most occurring characters

ValueCountFrequency (%)
d 11587
16.1%
a 11423
15.9%
y 9994
13.9%
u 5598
7.8%
e 4752
6.6%
s 4574
 
6.4%
r 4406
 
6.1%
n 4022
 
5.6%
T 2981
 
4.1%
S 2617
 
3.6%
Other values (7) 10043
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62003
86.1%
Uppercase Letter 9994
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 11587
18.7%
a 11423
18.4%
y 9994
16.1%
u 5598
9.0%
e 4752
7.7%
s 4574
 
7.4%
r 4406
 
7.1%
n 4022
 
6.5%
i 1562
 
2.5%
t 1429
 
2.3%
Other values (2) 2656
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
T 2981
29.8%
S 2617
26.2%
W 1593
15.9%
F 1562
15.6%
M 1241
12.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 71997
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 11587
16.1%
a 11423
15.9%
y 9994
13.9%
u 5598
7.8%
e 4752
6.6%
s 4574
 
6.4%
r 4406
 
6.1%
n 4022
 
5.6%
T 2981
 
4.1%
S 2617
 
3.6%
Other values (7) 10043
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 11587
16.1%
a 11423
15.9%
y 9994
13.9%
u 5598
7.8%
e 4752
6.6%
s 4574
 
6.4%
r 4406
 
6.1%
n 4022
 
5.6%
T 2981
 
4.1%
S 2617
 
3.6%
Other values (7) 10043
13.9%

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size931.5 KiB
2017
3345 
2016
2578 
2015
2131 
2014
1940 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters39976
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2017 3345
33.5%
2016 2578
25.8%
2015 2131
21.3%
2014 1940
19.4%

Length

2023-06-13T02:04:28.420039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T02:04:28.587440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2017 3345
33.5%
2016 2578
25.8%
2015 2131
21.3%
2014 1940
19.4%

Most occurring characters

ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3345
 
8.4%
6 2578
 
6.4%
5 2131
 
5.3%
4 1940
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3345
 
8.4%
6 2578
 
6.4%
5 2131
 
5.3%
4 1940
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 39976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3345
 
8.4%
6 2578
 
6.4%
5 2131
 
5.3%
4 1940
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3345
 
8.4%
6 2578
 
6.4%
5 2131
 
5.3%
4 1940
 
4.9%

month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7893736
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.2 KiB
2023-06-13T02:04:28.729325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3229042
Coefficient of variation (CV)0.42659453
Kurtosis-0.98257695
Mean7.7893736
Median Absolute Deviation (MAD)2
Skewness-0.4376743
Sum77847
Variance11.041692
MonotonicityNot monotonic
2023-06-13T02:04:28.878324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 1449
14.5%
11 1436
14.4%
9 1356
13.6%
10 831
8.3%
6 764
7.6%
7 721
7.2%
5 694
6.9%
8 674
6.7%
4 653
6.5%
3 652
6.5%
Other values (2) 764
7.6%
ValueCountFrequency (%)
1 440
 
4.4%
2 324
 
3.2%
3 652
6.5%
4 653
6.5%
5 694
6.9%
6 764
7.6%
7 721
7.2%
8 674
6.7%
9 1356
13.6%
10 831
8.3%
ValueCountFrequency (%)
12 1449
14.5%
11 1436
14.4%
10 831
8.3%
9 1356
13.6%
8 674
6.7%
7 721
7.2%
6 764
7.6%
5 694
6.9%
4 653
6.5%
3 652
6.5%

month_name
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size956.3 KiB
December
1449 
November
1436 
September
1356 
October
831 
June
764 
Other values (7)
4158 

Length

Max length9
Median length7
Mean length6.5403242
Min length3

Characters and Unicode

Total characters65364
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNovember
2nd rowNovember
3rd rowJune
4th rowOctober
5th rowOctober

Common Values

ValueCountFrequency (%)
December 1449
14.5%
November 1436
14.4%
September 1356
13.6%
October 831
8.3%
June 764
7.6%
July 721
7.2%
May 694
6.9%
August 674
6.7%
April 653
6.5%
March 652
6.5%
Other values (2) 764
7.6%

Length

2023-06-13T02:04:29.044247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
december 1449
14.5%
november 1436
14.4%
september 1356
13.6%
october 831
8.3%
june 764
7.6%
july 721
7.2%
may 694
6.9%
august 674
6.7%
april 653
6.5%
march 652
6.5%
Other values (2) 764
7.6%

Most occurring characters

ValueCountFrequency (%)
e 13206
20.2%
r 7465
 
11.4%
b 5396
 
8.3%
m 4241
 
6.5%
u 3597
 
5.5%
c 2932
 
4.5%
t 2861
 
4.4%
a 2550
 
3.9%
o 2267
 
3.5%
y 2179
 
3.3%
Other values (16) 18670
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55370
84.7%
Uppercase Letter 9994
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13206
23.9%
r 7465
13.5%
b 5396
9.7%
m 4241
 
7.7%
u 3597
 
6.5%
c 2932
 
5.3%
t 2861
 
5.2%
a 2550
 
4.6%
o 2267
 
4.1%
y 2179
 
3.9%
Other values (8) 8676
15.7%
Uppercase Letter
ValueCountFrequency (%)
J 1925
19.3%
D 1449
14.5%
N 1436
14.4%
S 1356
13.6%
M 1346
13.5%
A 1327
13.3%
O 831
8.3%
F 324
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 65364
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13206
20.2%
r 7465
 
11.4%
b 5396
 
8.3%
m 4241
 
6.5%
u 3597
 
5.5%
c 2932
 
4.5%
t 2861
 
4.4%
a 2550
 
3.9%
o 2267
 
3.5%
y 2179
 
3.3%
Other values (16) 18670
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13206
20.2%
r 7465
 
11.4%
b 5396
 
8.3%
m 4241
 
6.5%
u 3597
 
5.5%
c 2932
 
4.5%
t 2861
 
4.4%
a 2550
 
3.9%
o 2267
 
3.5%
y 2179
 
3.3%
Other values (16) 18670
28.6%

Interactions

2023-06-13T02:04:14.566268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:53.491811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:55.313626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:58.568826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:00.556608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:02.590297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:05.788696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:08.698888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:10.646788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:12.632670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:14.737247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:53.641584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:55.496272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:58.749344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:00.726391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:02.898216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:06.104901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:08.897531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:10.822338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:12.799396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:14.917109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:53.813390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:55.679350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:58.962272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:00.903061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:03.162262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:06.464197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:09.107029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:11.034236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:13.010157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:15.111835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:54.012008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:55.885601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:59.203777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:01.099540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:03.462906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:06.804092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:09.305287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:11.243831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:13.209307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:15.294493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:54.212596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:56.090237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:59.394001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:01.297110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:03.801404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:07.562022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:09.484259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:11.429832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:13.420540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:15.465377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:54.406840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:56.282638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:59.605410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:01.505411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:04.127150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:07.766183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:09.682374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:11.633442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:13.591862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:15.649477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:54.608442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:56.478517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:59.798672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:01.692692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:04.460517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:07.963655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:09.901509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:11.836880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:13.792637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:15.832686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:54.784636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:56.636815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:59.978861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:01.879066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:04.795497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:08.143832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:10.079823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:12.038435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:14.018292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:16.420256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:54.976397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:56.827833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:00.185950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:02.081346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:05.126064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:08.348402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:10.303957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:12.254340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:14.221771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:16.582512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:55.140710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:03:58.372272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:00.362687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:02.319472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:05.467818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:08.518354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:10.466475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:12.437611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-13T02:04:14.395906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-06-13T02:04:29.217157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
salesquantitydiscountprofitno_of_days_for_shippingsale_price_per_unitprofit_per_unitpurchase_price_per_unitpurchase_pricemonthship_modesegmentstateregioncategorysub-categoryweekday_stringyearmonth_name
sales1.0000.327-0.0570.518-0.0150.9350.4700.9110.9760.0160.0000.0020.0000.0000.0720.1420.0120.0000.008
quantity0.3271.000-0.0010.2340.016-0.0070.000-0.0050.3210.0240.0000.0120.0040.0000.0000.0000.0010.0150.011
discount-0.057-0.0011.000-0.543-0.014-0.070-0.5530.0870.088-0.0020.0270.0050.3540.2940.3770.3530.0170.0000.007
profit0.5180.234-0.5431.000-0.0070.4600.9530.3240.3850.0160.0050.0000.0170.0210.0560.1300.0060.0000.000
no_of_days_for_shipping-0.0150.016-0.014-0.0071.000-0.024-0.015-0.023-0.0140.0040.7810.0430.1020.0410.0000.0010.1410.0460.053
sale_price_per_unit0.935-0.007-0.0700.460-0.0241.0000.5050.9720.9130.0080.0190.0000.0260.0000.0990.1860.0070.0080.012
profit_per_unit0.4700.000-0.5530.953-0.0150.5051.0000.3670.3390.0120.0140.0000.0410.0220.0680.1640.0140.0000.014
purchase_price_per_unit0.911-0.0050.0870.324-0.0230.9720.3671.0000.9380.0060.0220.0000.0000.0100.0620.1760.0150.0000.013
purchase_price0.9760.3210.0880.385-0.0140.9130.3390.9381.0000.0140.0000.0000.0000.0050.0450.1280.0120.0000.000
month0.0160.024-0.0020.0160.0040.0080.0120.0060.0141.0000.0490.0360.0970.0430.0000.0000.0490.0211.000
ship_mode0.0000.0000.0270.0050.7810.0190.0140.0220.0000.0491.0000.0330.0960.0220.0000.0070.1050.0210.050
segment0.0020.0120.0050.0000.0430.0000.0000.0000.0000.0360.0331.0000.0900.0000.0000.0000.0380.0270.037
state0.0000.0040.3540.0170.1020.0260.0410.0000.0000.0970.0960.0901.0000.9980.0190.0000.1030.0890.101
region0.0000.0000.2940.0210.0410.0000.0220.0100.0050.0430.0220.0000.9981.0000.0000.0000.0600.0210.046
category0.0720.0000.3770.0560.0000.0990.0680.0620.0450.0000.0000.0000.0190.0001.0000.9990.0000.0000.000
sub-category0.1420.0000.3530.1300.0010.1860.1640.1760.1280.0000.0070.0000.0000.0000.9991.0000.0000.0000.000
weekday_string0.0120.0010.0170.0060.1410.0070.0140.0150.0120.0490.1050.0380.1030.0600.0000.0001.0000.0460.053
year0.0000.0150.0000.0000.0460.0080.0000.0000.0000.0210.0210.0270.0890.0210.0000.0000.0461.0000.028
month_name0.0080.0110.0070.0000.0530.0120.0140.0130.0001.0000.0500.0370.1010.0460.0000.0000.0530.0281.000

Missing values

2023-06-13T02:04:16.925277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-13T02:04:17.547529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idorder_dateship_dateship_modecustomer_idcustomer_namesegmentcitystateregionproduct_idcategorysub-categoryproduct_namesalesquantitydiscountprofitno_of_days_for_shippingsale_price_per_unitprofit_per_unitpurchase_price_per_unitpurchase_priceweekday_stringyearmonthmonth_name
0CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerHendersonKentuckySouthFUR-BO-10001798FurnitureBookcasesBush Somerset Collection Bookcase261.9600020.0000041.913603130.9800020.95680110.02320220.04640Friday201611November
1CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerHendersonKentuckySouthFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back731.9400030.00000219.582003243.9800073.19400170.78600512.35800Friday201611November
2CA-2016-1386882016-06-122016-06-16Second ClassDV-13045Darrin Van HuffCorporateLos AngelesCaliforniaWestOFF-LA-10000240Office SuppliesLabelsSelf-Adhesive Address Labels for Typewriters by Universal14.6200020.000006.8714047.310003.435703.874307.74860Thursday20166June
3US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerFort LauderdaleFloridaSouthFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table957.5775050.45000-383.031007191.42550-76.60620268.031701340.15850Sunday201510October
4US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerFort LauderdaleFloridaSouthOFF-ST-10000760Office SuppliesStorageEldon Fold 'N Roll Cart System22.3680020.200002.51640711.084001.258209.8258019.65160Sunday201510October
5CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerLos AngelesCaliforniaWestFUR-FU-10001487FurnitureFurnishingsEldon Expressions Wood and Plastic Desk Accessories, Cherry Wood48.8600070.0000014.1694056.980002.024204.9558034.69060Saturday20146June
6CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerLos AngelesCaliforniaWestOFF-AR-10002833Office SuppliesArtNewell 3227.2800040.000001.9656051.820000.491401.328605.31440Saturday20146June
7CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerLos AngelesCaliforniaWestTEC-PH-10002275TechnologyPhonesMitel 5320 IP Phone VoIP phone907.1520060.2000090.715205151.1586715.11920136.03947816.23680Saturday20146June
8CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerLos AngelesCaliforniaWestOFF-BI-10003910Office SuppliesBindersDXL Angle-View Binders with Locking Rings by Samsill18.5040030.200005.7825056.101331.927504.1738312.52150Saturday20146June
9CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerLos AngelesCaliforniaWestOFF-AP-10002892Office SuppliesAppliancesBelkin F5C206VTEL 6 Outlet Surge114.9000050.0000034.47000522.980006.8940016.0860080.43000Saturday20146June
order_idorder_dateship_dateship_modecustomer_idcustomer_namesegmentcitystateregionproduct_idcategorysub-categoryproduct_namesalesquantitydiscountprofitno_of_days_for_shippingsale_price_per_unitprofit_per_unitpurchase_price_per_unitpurchase_priceweekday_stringyearmonthmonth_name
9984CA-2015-1002512015-05-172015-05-23Standard ClassDV-13465Dianna VittoriniConsumerLong BeachNew YorkEastOFF-LA-10003766Office SuppliesLabelsSelf-Adhesive Removable Labels31.50000100.0000015.1200063.150001.512001.6380016.38000Saturday20155May
9985CA-2015-1002512015-05-172015-05-23Standard ClassDV-13465Dianna VittoriniConsumerLong BeachNew YorkEastOFF-SU-10000898Office SuppliesSuppliesAcme Hot Forged Carbon Steel Scissors with Nickel-Plated Handles, 3 7/8" Cut, 8"L55.6000040.0000016.12400613.900004.031009.8690039.47600Saturday20155May
9986CA-2016-1257942016-09-292016-10-03Standard ClassML-17410Maris LaWareConsumerLos AngelesCaliforniaWestTEC-AC-10003399TechnologyAccessoriesMemorex Mini Travel Drive 64 GB USB 2.0 Flash Drive36.2400010.0000015.22080436.2400015.2208021.0192021.01920Monday201610October
9987CA-2017-1636292017-11-172017-11-21Standard ClassRA-19885Ruben AusmanCorporateAthensGeorgiaSouthTEC-AC-10001539TechnologyAccessoriesLogitech G430 Surround Sound Gaming Headset with Dolby 7.1 Technology79.9900010.0000028.79640479.9900028.7964051.1936051.19360Tuesday201711November
9988CA-2017-1636292017-11-172017-11-21Standard ClassRA-19885Ruben AusmanCorporateAthensGeorgiaSouthTEC-PH-10004006TechnologyPhonesPanasonic KX - TS880B Telephone206.1000050.0000055.64700441.2200011.1294030.09060150.45300Tuesday201711November
9989CA-2014-1104222014-01-212014-01-23Second ClassTB-21400Tom BoeckenhauerConsumerMiamiFloridaSouthFUR-FU-10001889FurnitureFurnishingsUltra Door Pull Handle25.2480030.200004.1028028.349331.367606.9817320.94520Thursday20141January
9990CA-2017-1212582017-02-262017-03-03Standard ClassDB-13060Dave BrooksConsumerCosta MesaCaliforniaWestFUR-FU-10000747FurnitureFurnishingsTenex B1-RE Series Chair Mats for Low Pile Carpets91.9600020.0000015.63320545.980007.8166038.1634076.32680Friday20173March
9991CA-2017-1212582017-02-262017-03-03Standard ClassDB-13060Dave BrooksConsumerCosta MesaCaliforniaWestTEC-PH-10003645TechnologyPhonesAastra 57i VoIP phone258.5760020.2000019.393205129.188009.69660119.49140238.98280Friday20173March
9992CA-2017-1212582017-02-262017-03-03Standard ClassDB-13060Dave BrooksConsumerCosta MesaCaliforniaWestOFF-PA-10004041Office SuppliesPaperIt's Hot Message Books with Stickers, 2 3/4" x 5"29.6000040.0000013.3200057.400003.330004.0700016.28000Friday20173March
9993CA-2017-1199142017-05-042017-05-09Second ClassCC-12220Chris CortesConsumerWestminsterCaliforniaWestOFF-AP-10002684Office SuppliesAppliancesAcco 7-Outlet Masterpiece Power Center, Wihtout Fax/Phone Line Protection243.1600020.0000072.948005121.5800036.4740085.10600170.21200Tuesday20175May

Duplicate rows

Most frequently occurring

order_idorder_dateship_dateship_modecustomer_idcustomer_namesegmentcitystateregionproduct_idcategorysub-categoryproduct_namesalesquantitydiscountprofitno_of_days_for_shippingsale_price_per_unitprofit_per_unitpurchase_price_per_unitpurchase_priceweekday_stringyearmonthmonth_name# duplicates
0US-2014-1501192014-04-232014-04-27Standard ClassLB-16795Laurel BeltranHome OfficeColumbusOhioEastFUR-CH-10002965FurnitureChairsGlobal Leather Highback Executive Chair with Pneumatic Height Adjustment, Black281.3720020.30000-12.058804140.53600-6.02940146.56540293.13080Sunday20144April2